Academic staff
A list of AIAI academic staff
| Member | Interests | ||
 |
Director of Institute |
Formal verification of hardware, software and cyber-physical systems. Formalised mathematics. Interactive theorem proving. Automation of formal reasoning. Convex optimisation. |
|
 |
Pavlos Andreadis | I am a Teacher in Informatics primarily focussing on Applied Machine Learning. Main interest is in Recommender Systems as a tool for Collaboration and Coordination. Educated in Operations Research and Production and Management Engineering. Honoured to be the runner-up Supervisor of the Year 2020, as voted for by the Edinburgh University Students' Association (EUSA). | |
 |
Vaishak Belle | Explainable AI, scalable probabilistic inference and learning, probabilistic programming, statistical relational learning, commonsence reasoning, automated planning, and unifying logic and probability more generally. | |
 |
Alan Bundy | The automatic construction, analysis and evolution of representations of knowledge and the automation of mathematical reasoning, with applications to reasoning about the correctness of computer software and hardware. | |
 |
My main field of research lies in AI Modelling, which spans areas such as interactive theorem proving, formal verification, process modelling, and machine learning, with an emphasis on interpretable and trustworthy models, applied to healthcare and other complex domains. |
||
 |
Kobi Gal | Artificial Intelligence, Machine Learning for Human-Computer Collaboration and Negotiation, Big data in Education, Plan and Goal Recognition, Collaborative Group Learning, Incentive Design for effective teamwork, Computational Cognitive Science, Intelligent and Adaptive Tutoring Systems, Computational Game Theory. | |
| Igor Goryanin | |||
 |
Fengxiang He |
Trustworthy AI, including deep learning theory, privacy-preserving learning, decentralised learning, algorithmic game theory, etc., and their applications in economics. |
|
 |
Nadin Kokciyan | AI for Privacy, Argument Mining, Computational Argumentation, AI Ethics, Multiagent systems, Knowledge Representation and Reasoning | |
 |
Wenda Li |
I am passionate about machine learning and (interactive) theorem proving, as I believe modern machine learning techniques can make proof assistants more accessible (for building reliable systems). Tackling complex theorem proving tasks can also advance models' reasoning capabilities. Apart from these, I have interest in verified symbolic computing and mechanised mathematics. |
|
 |
Tiejun Ma | Dr Ma’s research focuses on risk analysis and decision-making using quantitative modelling and real-time Big data analysis techniques applies to fintech, cyber-risk, and resilience. He employs applied data science and mathematical modelling methodologies in the analysis/forecasts of risks, using an inter-disciplinary research strategy, via state-of-the-art computing, data analytics and behavioural analysis techniques. | |
 |
Valerio Restocchi | Modelling and simulation of complex socio-economic systems, Interaction and propagation on social networks, Modelling of people's behaviour, Financial markets and Econophysics, Cryptocurrencies and Fintech, Agent-based simulations, Network science. | |
 |
Michael Rovatsos | Ethical, human-friendly and responsible AI, multiagent systems, social computation. | |
 |
Siddharth N. |
My research broadly involves the confluence of computer vision, robotics, natural-language processing, cognitive science, and elements of neuroscience. It seeks to better understand perception and cognition with a view to enabling human-intelligible machine intelligence through learning structured and interpretable representations of perceptual data. Explainable AI; Interpretable ML; Probabilistic Programming; Approximate Inference; Human-Machine Interaction; Neural-Symbolic Systems; Computer Vision; NLP |
|
 |
Aurora Constantin |
|
|